CAD Sensitization, an Easy Way to Integrate Artificial Intelligence in Shipbuilding

A case is made that introducing CAD-based geometry prematurely in the aircraft design process actually hinders multidisciplinary design. Consequently, a design process which allows a designer to quickly build and analyze aerospace configurations without CAD or computational meshing specialists is proposed. This allows higher fidelity analyses to be applied at conceptual design and decreases the turnaround time for trade studies and optimizations. The basic premise of conceptual geometry modeling is presented and discussed. Key features and capabilities which are essential for a successful geometry modeler are identified. Desktop Aeronautics' conceptual geometry modeler, RAGE, is described, with emphasis on recent progress and advances in the tool. A preliminary graphical-user-interface is presented along with an application programming interface. Extensibility of the RAGE modeler is discussed along with the importance of this feature. The applicability of RAGE to simple trade studies is demonstrated in an example problem. The precision of the RAGE modeler is also demonstrated on a complex aircraft design. Future plans for the geometry modeler are also discussed. I. Background In the conceptual phase of aircraft design, there is often a wide range of geometric configurations that the designer wishes to consider, analyze, and assess. A designer’s intuition and creativity are crucial to a successful conceptual design, yet are easily inhibited by the modeling and analysis process itself, particularly if high-fidelity analysis is warranted. If the time between inception and assessment of a candidate design is relatively long, a designer may be forced to limit the number of concepts considered. New data revealed in a design iteration may suggest or require significant changes to the geometry configuration. Since the majority of considered design configurations will eventually be discarded, an ideal conceptual design process should involve a highly efficient and easy to use geometry modeler. Significant changes should be allowed without tedious re-work of the geometry model. Exporting the model for analysis should require little effort and have the capability to be automated. Therefore, any modeling application used in this process should be flexible and not restrict the designer’s choice of user interface, analysis method, and design optimization environment. Most current design processes in the aerospace industry rely heavily on computer-aided-design (CAD) applications. As a general design tool, CAD is a powerful geometry modeler capable of precisely representing practically any shape. Unfortunately, this generality is realized at the cost of requiring specialized training to efficiently and effectively use the software. Also, a CAD model is often not easily modified, especially if significant re-work of the geometry is necessary. This can lead to the premature freeze of an aircraft design due to employing excessively high-fidelity geometry modeling too early in the design process. Parametric CAD modeling could address this issue, but creating a well-designed parametric CAD model is difficult. Not only does it require an extremely skilled operator, but also the designer must have some insight into what parameterization may ultimately be desired. Modifying a parametric CAD model is often more difficult than modifying a conventional CAD model. Significant changes, such as adding or removing cross-sections, may invalidate the parameterization in some cases. In most organizations, CAD modeling (whether it be parametric or conventional) is often assigned to a dedicated CAD specialist. Since the conceptual designer is usually not a CAD specialist, the latter must communicate his design intent to the former, often using sketches, dimensions, and word of mouth. When the CAD specialist interprets the sketches, any part or detail not precisely defined usually requires judgment and artistic license. This interpretation may result in loss of design intent and indeterminate iteration between the CAD specialist and designer to obtain a satisfactory result. Because this process is iterative, CAD operators often must recreate similar geometry

Since the emergence of Computer Aided Design (CAD) software in 1957, educators have been committed to equipping design engineering students with proficiency in the most widely employed CAD tools in the industry. In the early days, training focused on step by step instructions and understanding CAD theory. Overtime, lecturers adopted more hands-on practice, project-based learning and collaborative learning techniques [1]. The abundance of online resources and tutorials has allowed lecturers, particularly in Higher Education (HE), to focus less on training and more on theory and use of various software and techniques. Over the last decade, the number, pace and emergence of unique sub-sector software development has grown exponentially, making it difficult for educators to adopt a fixed CAD curriculum. In the early days, the dominant players in the CAD software arena until the early 2010s were Solidworks, Rhino 3D, and Autodesk Maya, each equipped with its own proprietary rendering engine. However, over the past decade, there has been a proliferation of CAD software alternatives, complemented by independent rendering engines like Keyshot, notably embraced by software applications such as Blender, Gravity sketch, and Sketchup. In particular, Blender and Rhino's Grasshopper add-on have experienced a surge in user-generated custom add-ons, broadening the array of available features and functionalities. The rapid expansion of Artificial Intelligence (AI) tools over the last two years has introduced a new layer of complexity, as tools such as Midjourney, Stable Diffusion, and Control Net have gained prominence in the industry. The exponential growth of computational tools available has presented higher education institutions with a perplexing choice regarding which CAD software to teach and the most effective pedagogical methods [2]. This complexity is further compounded by the dynamic nature of software development and the diverse career paths of students, each demanding a distinct skill set for success. This intricate situation prompts the inquiry into an approach to future-proof and tailor CAD software education to the unique requirements of each student cohort . Perhaps the same tool that advances learning can be used for future-proofing? Whilst there are examples of successful uses of AI in education in other disciplines, the inquiry using baseline data from undergraduate and postgraduate students at a world leading institution raises additional questions: • Can we design an AI tool that develops bespoke CAD learning for students? • Can this learning go beyond simple command instructions and extract higher universal principles of CAD theory? • Specifically in HE, can AI be used to create individual learning plans based on student’s interests, industry needs and ongoing advances in CAD? References : [1] Brink, Kilbrink & Gericke, 2023. Teach to Use CAD or through Using CAD: An Interview Study with Technology Teachers. International journal of technology and design education 33.3: 957–979. [2] Xie, 2018. Learning and Teaching Engineering Design through Modeling and Simulation on a CAD Platform. Computer applications in engineering education 26.4: 824–840. [3] Ye, Peng, Chen & Cai, 2004. Today's students, tomorrow's engineers: an industrial perspective on CAD education, Computer-Aided Design, Volume 36, Issue 14, 2004, 1451-1460.

Future prospects of computer-aided design (CAD) – A review from the perspective of artificial intelligence (AI), extended reality, and 3D printing

Oracles, draughtsmen, and agents: the nature of knowledge and creativity in design and the role of IT

Evolutionary design assistants for architecture

Evolutionary design assistants for architecture

Intelligent CAD 2.0

This presentation will illustrate how to expand the view by considering the total life cycle in an efficient way into the decision making process and why it is important to do so. The business case will show, how the ecological and economic aspects considering the total life cycle of different design options have been considered when determining the preferable design options out of an holistic point of view. Life Cycle Engineering (LCE)/ Life Cycle Assessment (LCA) integrated in the design Process LCE methodology is evaluating ecological, technical and economic aspects considering the total life cycle of processes/products. LCA studies are the basis for the ecological evaluation within LCE. LCE studies are based on material and energy flow information needed while running the facilities or for producing products. LCE is a simulation tool show optimization potentials as well as supporting the decision making process within the design phase. As various databases hold information on ecological impacts of material- and energy production and information on the economic values is available within the involved companies, time consuming research on basic materials and energies is not necessary. Therefore first estimations on scenarios can be made within days to support the decision process not causing any time delay. LCE studies can be conducted within the design process and on existing facilities/products. If LCE is used within the design process optimization potentials can be shown in early stages of the design phase of facilities/products. Integration of LCE within early stages of the design ensures an efficient way of improving the ecological profile of processes and products and reducing the overall costs considering the total life cycle. Realization within a software tool The software tool GaBi4 is developed and designed to support LCE efficiently and in a transparent way. The design of the facilities can be modeled according to the material and energy flow. This enables the user to run scenario analysis for different design options based on the process flow model. Business case The methodology of LCE has been integrated into the design process of the new rear axle paint shop focusing on the handling of the overspray. Different design options have been analyzed and arguments were made explicit to support the decision making process. As LCE was part of the whole design process from the beginning, the effort for all participants could have been minimized. Conclusions The case study has shown that the integration of LCE into the design process provides additional information and is not causing any delay of the decision making process. LCE enables a transparent presentation of the economics and ecological impacts on a process bases. Optimization potentials, ecological and economic, can be shown at all stages of the design phase and result in reducing the overall costs and environmental burdens caused by the paint process.

The article presents application of the FreeCAD software and generative artificial intelligence (AI) in the design process regarding exemplary mechanical engineering-related assemblies. Authors have studied how to support a product development and computer-aided (CAD) design phase by the use of generative pre-trained transformer (GPT). Prompting process, that lead to Python language-based code creation in the ChatGPTTM by OpenAI company, was studied from the perspective of 3D assemblies generation in the FreeCAD software. Moreover, authors studied how to improve the basic 3D models by the use of text-based prompts and CAD user improvements of the solid models and discussed the general effectiveness of this approach. It was also separately studied how to automate creation of many parts of the same type by the use of the AI, and apply such CAD libraries in the developed assemblies. The performed research presented selected possibilities of AI-based design process, challenges of the design process and future areas of investigation of AI-based assembly development resulting from the usage of existing GPTs.

Environmentally conscious manufacturing process (ECMP) has become an obligation to the environment and to the society itself, enforced primarily by governmental regulations and customer perspective on environmental issues. ECMP involves integrating environmental thinking into new product development. This is especially true in the computer-aided design (CAD) phase which is the last phase in the design process. At this stage, more than 80 % of choices are done. Feature technology (FT), the core of the CAD phase, is then used to integrate environmental aspects. Also, integrations realized on CAD systems, such as computer-aided manufacturing (CAM), computer-aided process plan (CAPP), and product life management (PLM), are explored to eco-design products. This paper presents a new methodology for the selection of a green manufacturing process based on CAD features and CAD/CAM/CAPP/PLM integrations. Firstly, we present an overview of works using FT in the CAD phase to reduce the environmental impacts of a manufacturing process. Secondly, we propose an approach based on FT for selecting an ECMP to promote a new simple eco-design tool which helps inexperienced eco-designers. Finally, a case study is considered to validate the proposed approach and results are commented.

Teaching computer-aided building design (CABD) in a project-based learning environment is an idea derived from the broader problem-based learning movement. However, a project-based learning curriculum has rarely been implemented in building design education, let alone in CABD. The idea of teaching CABD through design-based approaches has been increasingly adopted over the past 20 years. However, most of the resulting experiments have been limited to specific courses within larger programmes. This approach has rarely affected a dedicated CABD curriculum. The development and implementation of a feasible project-based learning CABD curriculum is described in this paper. In this programme the emphasis is placed on the building aspects and/or objectives of the design process rather than on the different products of the design phases or even on software categories.

This study examines the potential of computer-aided design (CAD) systems equipped with artificial intelligence (AI) in reducing the workload of dental technicians. We aimed to compare the accuracy and design time of crowns designed using conventional CAD with those designed using AI-equipped CAD. Abutment tooth models of a maxillary right second premolar (FDI classification #15) and a maxillary left first molar (FDI classification #26) were mounted on a dental model to form the master model. Stereolithography data were acquired using an intraoral scanner, and five dental technicians designed one crown each for #15 and #26 using both conventional and AI-equipped CAD systems. With the #15 and #26 crowns, six measuring points were established for comparing the accuracy of the occlusal surfaces and design time of the crowns designed by the two CAD systems. The occlusal surfaces were also compared for the buccal and palatal sides. The accuracy of the occlusal surface was 275.5 ± 116.8 µm and 25.7 ± 13 µm for the conventional CAD and AI-equipped CAD systems, respectively. For the buccal and palatal surface comparisons, the conventional CAD system revealed larger misfits on the palatal side for both #15 and #26, with significant differences observed. No significant differences were noted with the AI-equipped CAD system. The AI-equipped CAD resulted in significantly faster design time for both #15 and #26. The AI-based CAD system significantly reduced design time and enabled the fabrication of uniform crowns regardless of the dental technician's experience and skill.

Since the introduction of scientific disciplines into the shipbuilding process, there has been a search for optimisation of human and material resources. The current environmental crisis is putting additional pressure on global resource management and special attention to materials sourcing and utilisation. This paper discusses the potential solution for the current lack of effectiveness at early design stages, which are still based on 2D drawings. The industry is demanding a new 3D approach, which implies, first, a change in the procedures, and second, having a suitable CAD/CAM tool for the early generation of a digital mock-up, from which the project is developed throughout all design and production phases, from conceptual design to operation. The proposed solution shown in this paper would improve the general arrangement definition, with the use of CAD for the 3D definition of the compartments and the main equipment positioning; improve the naval architecture calculation; and finally improve the basic/class design stage, with better reuse of data already developed in the general arrangement model and in the definition of a 3D model of structure, with the main equipment positioning, pipes, and electrical equipment already into the model. Intelligent P&I diagrams and single-wire electric diagrams would be used at this stage and connected to the 3D model. Additionally, it will allow for early estimating of materials, weights, and associated processes. This will be further supported by the use of topology to consider design alternatives and produce early information on materials for procurement and production. The cost reduction associated with the definition of the early design stages in 3D has been estimated at around 15% of the overall design and production stages.

The constant progress of technique is inevitable nowadays and seems to be the same in a predictable future. The observation of this phenomenon leads us to formulate a few reflections on it. The use of advanced techniques causes a clearer detachment of production processes from human work. Robotics and automation were initially supposed to facilitate the production of elements, but over time they began to replace humans more and more. Will there be a place for human work in the future? It is already being pushed out of many bastions in which it was supposed to be indispensable. Autonomous cars and buses are the best example of this. Can similar phenomena be noticed in design? Will the machine replace the creator? The development of artificial intelligence (AI) shows that it is possible. Complicated algorithms are already able to compose a piece of classical music. In the case of architecture, architects are still in the lead, however, one has to take into account the conquest of this field by AI. At the moment, designers have various advanced techniques at their disposal to facilitate and accelerate their work. The most important among them are: digital 3D modeling CAD (Computer Aided Design), Building Information Modeling (BIM), visualizations, and computer animations mainly used to present ready-made ideas, but also useful at the concept stage. Apart from them, three-dimensional printing is also important, as well as three-dimensional design of structures. The above technologies are increasingly used in the design process. They are more compatible with each other than before. They allow you to save labour, accelerate the implementation of tasks, as well as to optimise the designed buildings in many respects related to construction, prefabrication or energy efficiency, to name just a few. An important, although not very common, advantage of technological innovations is their use not only during design and construction, but also during the maintenance of ready-made buildings. The best example of this is BIM, which facilitates the previous management of these technology designed objects. In the future, it will be much easier to design the adaptations of such buildings and to store information of changes which were made. This approach fits in with the idea of Management of Change, which can be included in an even broader aspect among the paradigm of sustainable development.

Artificial Intelligence (AI) is currently one of the most talked-about technologies, both among scientists and in public media. Several factors have contributed to its development in recent years. The first is access to vast quantities of data, such as in the industrial field, the advent of Industry 4.0, which promotes automation and data sharing in several technologies. Another factor is the continuous improvement in computing power thanks to the development of ever more powerful processors and the optimization of algorithms. With these two limitations removed, the focus of most AI developments is on the quality of predictions. The integration of AI into the industrial domain represents an exciting new frontier for innovation. Just as AI has transformed many other sectors, its application to mechanical technologies enables significant improvements in design, manufacturing and quality control processes: from computer-aided design (CAD) to printing parameter optimization, defect detection and real-time monitoring. This type of technology requires computer systems, data with management systems and advanced algorithms which can be used by AIs. In mechanical engineering, AI offers many possibilities in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration, etc. Methods and Applications of Artificial Intelligence is dedicated to the methods and applications of AI in mechanical engineering. Each chapter clearly sets out the techniques used and developed and accompanies them with illustrative examples. The book is aimed at students but is also a valuable resource for practicing engineers and research lecturers.